In the above contributions, NNs are generally treated as black-boxes,
which does not guarantee optimal performance and leads to limited
interpretability, along with limited control. Furthermore, in massive
MIMO systems, the training overhead of these methods is expensive due
to the multi-dimensional training samples and long training time. To
overcome these issues, a model-driven network, which is referred to as
deep-unfolding, has been proposed in [1]}. This method
generally unfolds iterative algorithms into multi-layer NN structures
and introduces a set of trainable parameters to accelerate convergence
and to increase the system performance. The authors
of [2]} and [3]} invoked this approach to unfold the gradient
descent (GD) algorithm for data detection.
In [4]}, [5]} the authors developed a deep-unfolding
method inspired by the normalized min-sum algorithm for the decoding
of polar codes. In [6]}, a scheme based on deep-unfolding
was put forward for sparse signal recovery. In [7]},
the authors employed the deep-unfolding method for automatically
optimizing the precoders relying on single-bit digital-to-analog
converters (DACs). The authors of [8]} adopted a
deep-unfolding NN based on the conjugate GD algorithm for constant
envelope precoding. A finite-alphabet precoder was developed in
[9]}, which unfolded the iterative discrete estimation
algorithm into a NN. In [10]}, the authors derived the
generalized chain rule (GCR) in matrix form and proposed a
deep-unfolding NN based on the WMMSE precoding design algorithm.
